1) simple discriminative matrix
简单相异矩阵
1.
Combining the rough set theory with hierarchic analysis model in view of their characteristics and introducing the definition of importance into non-core attributes,a new data reduction algorithm is proposed using a simple discriminative matrix.
结合粗糙集理论的优势和层次分析模型的特点并将两者有机地结合起来,通过在非核属性中引入重要性概念并利用简单相异矩阵,提出了基于粗糙集理论和层次分析的数据约简算法,同时证明了该算法的有效性和完备性。
2.
This paper proposes a method of the simple discriminative matrix from these two methods ,and applies it into data reduction.
本文从以上两种方法出发,提出简单相异矩阵,并用以进行数据约简。
2) simple matrix
简单矩阵
4) dissimilarity matrix
相异度矩阵
1.
0],the dissimilarity of two alerts was represented by using a dissimilarity matrix;the more excellent clustering centers were chosen by the genetic algorithm,and the similar alerts would be clustered according to the dissimilarity matrix.
0]上,两报警间的相异程度用一个相异度矩阵表示;利用遗传算法的自适应优化特性选取较优的聚类中心,根据报警间的相异度矩阵将相似的报警进行聚类;在此基础上,分别对每一类中的报警采用凝聚层次的聚合方法进行聚合。
5) trace_different matrix
迹相异矩阵
6) simplified information-matrix
简单信息矩阵
补充资料：相异
1.见"相异"。
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